AIF-C01 Applications of Foundation Models Practice Question
A research team is using Amazon Bedrock to analyze scientific papers. They want the model to generate answers based only on papers published after 2023. Which approach should they use?
⚠ Common exam trap
AWS often tests the misconception that a system prompt or fine-tuning can reliably restrict a model's knowledge to a specific time period, when in fact only a retrieval-based approach with metadata filtering can enforce such temporal constraints.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Amazon Bedrock Knowledge Bases with a metadata filter to retrieve only papers published after 2023, and generate responses based on retrieved content.
Amazon Bedrock Knowledge Bases with a metadata filter allows you to restrict retrieval to only documents that match specific metadata criteria, such as publication year. By filtering the vector search to only include papers published after 2023, the model generates responses based solely on that retrieved content, ensuring it does not rely on pre-2023 data. This approach is the only one that guarantees the model's answers are grounded exclusively in the specified time range.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune the model on a dataset of post-2023 papers and deploy it.
Why it's wrong here
Fine-tuning adjusts weights and style but does not guarantee the model ignores pre-2024 knowledge, and Bedrock fine-tuning cannot enforce a strict publication-date boundary at inference. It is tempting for domain adaptation, such as teaching specialised terminology, but retrieval with metadata filtering is the mechanism that actually restricts source documents.
- ✗
Set the maxTokens to a low value to force the model to rely on recent context.
Why it's wrong here
maxTokens caps output length only; it has no bearing on which training or context data the model uses, so pre-2024 knowledge remains available. It is tempting as a cost or verbosity control, such as limiting summary length, but it cannot filter documents by publication date.
- ✗
Include a system prompt instructing the model to ignore data before 2023.
Why it's wrong here
A system prompt cannot restrict the model's parametric knowledge to post-2023 papers; the model may still draw on pre-2024 training data. System prompts are tempting for lightweight behavioural steering, such as tone or output format, but guaranteeing a hard temporal cutoff requires retrieval filtering, not instruction alone.
- ✓
Use Amazon Bedrock Knowledge Bases with a metadata filter to retrieve only papers published after 2023, and generate responses based on retrieved content.
Why this is correct
Amazon Bedrock Knowledge Bases with a metadata filter satisfies the post-2023 constraint by restricting vector retrieval to documents whose publication-date metadata matches the filter, so the foundation model generates answers grounded solely in that retrieved subset rather than its training data. This enforces the temporal restriction at retrieval time, preventing older papers from entering the context window.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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